ITS Fairy improves vehicle safety by supplying missing object info
ITS Fairy: Occlusion Assistance Selected Against a Recipient's Own Perception Reports
Networking and Internet ArchitectureDistributed, Parallel, and Cluster Computing
Summary
Vehicles use their own sensors to detect nearby objects and avoid crashes, but sometimes their view is blocked. This paper introduces ITS Fairy, a system that helps by sending vehicles only the information about important objects they missed. Instead of guessing what a vehicle can see, ITS Fairy looks at what the vehicle itself already reported and fills in the gaps. Tests in traffic simulations show that this approach helps vehicles keep safer distances at tricky spots like merges and intersections.
What this means in practice
- •For automotive safety engineers: Enhance collision avoidance by providing only the missing relevant object states that a vehicle failed to detect due to occlusion.
- •For urban traffic management teams: Improve infrastructure-based traffic safety services by sharing selective object data to assist vehicles during complex maneuvers like merges and intersections.
Authors
Yenan Wang, Oscar Karlsson, Elad Michael Schiller, Francesco Raviglione, Claudio Casetti
Abstract
Cooperative perception can expose object state beyond a vehicle's onboard sensors, but sensing occlusion can still leave a local safety application without the objects its collision computation needs. To tackle this challenge, we present the ITS Fairy, an infrastructure-side Server Local Dynamic Map (S-LDM) service whose decision unit is the pair (recipient, missing conflict-relevant object): among objects absent from a recipient's CPM-derived reported awareness, it sends only those relevant to a Time of Closest Approach (TCA) conflict test. Comparable services predict what a vehicle can perceive; the ITS Fairy instead reads what it has already reported. The recipient inserts the selected state into its local LDM and uses its unchanged collision-avoidance controller. We evaluate this application-level mechanism in SUMO--ms-van3t--S-LDM emulation, since extended as VaN3Twin, using a sensing-occluded lane merge and four-way intersection scenario. At every main-sweep speed, the smallest assisted per-encounter minimum TCA exceeds the largest local-only value in the archived data. Additionally, assisted medians remain in the multi-second range where local-only operation repeatedly approaches zero. In the lane-merge robustness data, the median benefit persists at 80% configured assistance omission with 10 and 5 Hz analysis, but largely disappears at 100-120 km/h when 80% omission is combined with 1 Hz analysis. These results demonstrate the application-level value of supplying object state selected against what a recipient has itself reported. They are not a vehicular wireless-channel evaluation, and they do not quantify what selectivity saves relative to forwarding every nearby object.